Point cloud segmentation (PCS) plays an essential role in robot perception and navigation tasks. To efficiently understand large-scale outdoor point clouds, their range image representation is commonly adopted. This image-like representation is compact and structured, making range image-based PCS models practical. However, undesirable missing values in the range images damage the shapes and patterns of objects. This problem creates difficulty for the models in learning coherent and complete geometric information from the objects. Consequently, the PCS models only achieve inferior performance. Delving deeply into this issue, we find that the use of unreasonable projection approaches and deskewing scans mainly leads to unwanted missing values in the range images. Besides, almost all previous works fail to consider filling in the unexpected missing values in the PCS task. To alleviate this problem, we first propose a new projection method, namely scan unfolding++ (SU++), to avoid massive missing values in the generated range images. Then, we introduce a simple yet effective approach, namely range-dependent $K$-nearest neighbor interpolation ($K$NNI), to further fill in missing values. Finally, we introduce the Filling Missing Values Network (FMVNet) and Fast FMVNet. Extensive experimental results on SemanticKITTI, SemanticPOSS, and nuScenes datasets demonstrate that by employing the proposed SU++ and $K$NNI, existing range image-based PCS models consistently achieve better performance than the baseline models. Besides, both FMVNet and Fast FMVNet achieve state-of-the-art performance in terms of the speed-accuracy trade-off. The proposed methods can be applied to other range image-based tasks and practical applications.

翻译：点云分割在机器人感知与导航任务中扮演着关键角色。为高效理解大规模室外点云，通常采用其距离图像表示法。这种类图像表示紧凑且结构化，使基于距离图像的点云分割模型具有实用性。然而，距离图像中不希望出现的缺失值会破坏物体的形状与模式，导致模型难以从物体中学习连贯完整的几何信息，从而使点云分割模型仅能达到次优性能。深入探究该问题后，我们发现不合理的投影方法与去畸变扫描是导致距离图像产生非必要缺失值的主要原因。此外，几乎所有先前工作均未在点云分割任务中考虑填补意外缺失值。为缓解该问题，我们首先提出一种新投影方法——scan unfolding++（SU++），以避免生成的距离图像中出现大量缺失值。然后引入一种简单有效的方法——距离相关K近邻插值（$K$NNI），以进一步填补缺失值。最后，我们提出缺失值填充网络（FMVNet）与快速FMVNet。在SemanticKITTI、SemanticPOSS和nuScenes数据集上的大量实验结果表明，通过采用所提出的SU++与$K$NNI，现有基于距离图像的点云分割模型性能持续优于基线模型。此外，FMVNet与快速FMVNet在速度-精度权衡方面均达到最先进水平。所提出的方法还可应用于其他基于距离图像的任务及实际应用场景。